MAKER-P Genome Annotation using Atmosphere (Images Tutorial)

Rationale and background:

MAKER-P is a flexible and scalable genome annotation pipeline that automates the many steps necessary for the detection of protein coding genes (Campbell et al. 2013). MAKER-P identifies repeats, aligns ESTs and proteins to a genome, producesab initiogene predictions, and automatically synthesizes these data into gene annotations having evidence-based quality indices. MAKER-P was developed by theYandell Lab. Its predecessor, MAKER, is described in several publications (Cantarel et al. 2008;Holt & Yandell 2011). Additional background is available at theMAKER Tutorial at GMOD and is highly recommended reading. MAKER-P v2.28 is currently available as an Atmosphere image and is MPI-enabled for parallel processing.

This tutorial will take users through steps of:

Launching the MAKER-P Atmosphere image

Uploading data from the CyVerse Data Store (optional)

Running MAKER-P on an example small genome assembly

What kind of data do I need?

Mandatory requirements

Genome assembly (fasta file)

Organism type

Eukaryotic (default, set as: organism_type=eukaryotic)

Prokaryotic (set as: organism_type=prokaryotic)

Additional data that can be used to improve the annotation (Highly recommended)

Part 1: Connect to an instance of an Atmosphere Image (virtual machine)

Step 2. Create a project and name the project name as MAKER-P and description as MAKER-P atmosphere tutorial

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Step 3. Click the project and then Select the image MAKER-P_2.31.9 and click Launch Instance. It will take ~10-15 minutes for the cloud instance to be launched.

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Note: Instances can be configured for different amounts of CPU, memory, and storage depending on user needs. This tutorial can be accomplished with the small instance size, medium1 (4 CPUs, 8 GB memory, 80 GB root)

Step 4. Once the VM is ready. Click the VM which will take onto next screen where you can open Web Shell

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or ssh into the ip address on a terminal like below.

Part 2: Set up a MAKER-P run using the Web Shell (Make sure you run all your analyses on volume. See below)

Step 1. Get oriented. You will find staged example data in folder "maker_test_data/" within the folder /opt". List its contents with the ls command:

maker_*.ctl file are a set of configuration files that can be used for this tutorial or generated Step 5.

The subdirectory "test_data" includes input data files that you will use for this tutorial.

The subdirectory "test_genome.maker.output" contains output that you should expect to see when completing the tutorial. Have a look at "test_data" directory:

fasta files include a scaled-down genome (test_genome.fasta) which is comprised of the first 300kb of 12 chromosomes of rice and scaled-down genome (test_genome_chr1.fasta) which is comprised of the first 300kb of first chromosome of rice

As the names suggest, the "/opt/maker/bin" directory includes many useful auxiliary scripts. For example cufflinks2gff3 will convert output from an RNA-seq analysis into a GFF3 file that can be used for input as evidence for MAKER-P. Both Cufflinks and cufflinks2gff3 are available as tools in the iPlant Discovery Environment (DE). Other auxiliary scripts now available in the DE include tophat2gff3, maker2jbrowse, and maker2zff. We recommend reading MAKER Tutorial at GMOD for more information about these.

Step 2. Set up a MAKER-P run. Create a working directory called "maker_test_data" on your home directory using the mkdir command and cd into that directory:

Step 3. Copy the directory "test_data" into the current directory using cp -r. Verify using the contents using ls command:

Step 4. Run the maker command with the -h flag to get a usage statement and list of options. (Ignore the line that says Argument is missing)

Step 5. Create control files that tell MAKER-P what to do. Three files are required:

To create these files run the maker command with the -CTL flag. Verify with ls:

The "maker_exe.ctl" is automatically generated with the correct paths to executables and does not need to be modified except for "snap" software. Run the following command to fix the file path for snap

The "maker_bopt.ctl" is automatically generated with reasonable default parameters and also does not need to be modified unless you want to experiment with optimization of these parameters.

The automatically generated "maker_opts.ctl" file does need to be modified in order to specify the genome file and evidence files to be used as input. Several text editors are available, including emacs, nano, and gedit.

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Note: If pressed for time, a pre edited version of the "maker_opts.ctl" file is staged in /opt/maker_test_data/. Delete the current file and copy the staged version here (see below). Then skip to Step 6. $ rm maker_opts.ctl $ cp /opt/maker_test_data/maker_opts.ctl .

Here are the sections of the "maker_opts.ctl" file you need to edit.

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Do not allow any spaces after the equal sign or anywhere else

The files can be present in same the directory as the "maker_opts.ctl" or make sure you use the relative path if the files are present in other directories

This section pertains to specifying the genome assembly to be annotated and setting organism type:

The following section pertains to EST and other mRNA expression evidence. Here we are only using same species data, but one could specify data from a related species using the altest parameter. With RNA-seq data aligned to your genome by Cufflinks or Tophat, one could use maker auxiliary scripts (cufflinks2gff3 and tophat2gff3) to generate GFF3 files and specify these using the est_gff parameter:

The following section pertains to protein sequence evidence. Here we are using previously annotated protein sequences. Another option would be to use SwissProt or other database:

This next section pertains to repeat identification:

Various programs for ab initio gene prediction can be specified in the next section. Here we are using SNAP set to use an HMM trained on rice, tRNA annoation has been turned on and finally specify the path to the rRNA file for finding snoRNAs with Snoscan

Step 8.Run MAKER-P

Make sure you are in the "maker_test_data" directory and all of your files are in place. Perform these steps to check:

Starting the MAKER-P run is as simple as entering the command maker. Because your maker control files are in the present directory, you do not have to explicitly specify these in the command; they will be found automatically.

MAKER-P automatically outputs thousands of lines of STDERR, reporting on its progress and producing warnings and errors if they arise. It is a good practice run the command in the background using nohup command.

Another useful practice is to employ the unix time command to report statistics on how long the run took to complete. The output of time will appear at the end of the captured standard error file.

Putting all of this together we type the following command to start MAKER-P:

Recommended

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If you have launched an Atmosphere instance with multiple CPUs, you can distribute MAKER-P across the each processor using mpiexec command. This example assumes that a user has checked out a "medium" instance size with 4 CPU. If you launched a bigger instance, change the value accordingly to take advantage of all the CPUs on the instance

MAKER-P should now be running. For this example, it usually takes about 12 minutes to complete (50x faster than non mpi run). Monitor progress and check for errors by examining the nohup.out file. You will know MAKER-P is finished when the log_file announces "Maker is now finished!!!". Use the tail command to look at the last 10 lines of the log_file

Step 10. Examine MAKER-P output.

Output data appears in a new directory called "test_genome.maker.output". Move to that directory and examine its contents:

The maker_opts.log, maker_exe.log, and maker_bopts.log files are logs of the control files used for this run of MAKER.

The mpi_blastdb directory contains FASTA indexes and BLAST database files created from the input EST, protein, and repeat databases.

test_genome_master_datastore_index.log contains information on both the run status of individual contigs and information on where individual contig data is stored.

The test_genome_datastore directory contains a set of subfolders, each containing the final MAKER output for individual contigs from the genomic fasta file.

Check the test_genome_master_datastore_index.log and task_outputs.txt to see if there were any failures:

All completed. Other possible status entries include:

FAILED - indicates a failed run on this contig, MAKER will retry these

RETRY - indicates that MAKER is retrying a contig that failed

SKIPPED_SMALL - indicates the contig was too short to annotate (minimum contig length is specified in maker_opt.ctl)

DIED_SKIPPED_PERMANENT - indicates a failed contig that MAKER will not attempt to retry (number of times to retry a contig is specified in maker_opt.ctl)

The actual output data is stored in in nested set of directories under* test_genome_datastore* in a nested directory structure.

A typical set of outputs for a contig looks like this: (Only few lines from the output are shown below)

The Chr6.gff file is in GFF3 format and contains the maker gene models and underlying evidence such as repeat regions, alignment data, and ab initio gene predictions, as well as fasta sequence. Having all of these data in one file is important to enable visualization of the called gene models and underlying evidence, especially using tools like Apollo which enable manual editing and curation of gene models.

The fasta files Chr6.maker.proteins.fasta and Chr6.maker.transcripts.fasta contain the protein and transcript sequences for the final MAKER gene calls.

The Chr6.maker.non_overlapping_ab_initio.proteins.fasta and Chr6.maker.non_overlapping_ab_initio.transcripts.fasta files are models that don't overlap MAKER genes that were rejected for lack of support.

The Chr6.maker.snap_masked.proteins.fasta and Chr6.maker.snap_masked.transcript.fasta are the initial SNAP predicted models not further processed by MAKER

The output directory theVoid.Chr1 contains raw output data from all of the pipeline steps. One useful file found here is the repeat-masked version of the contig, query.masked.fasta.

Step 8: Merge the gff files to get a combined gff2 file which can be used for all downstream analysis

-d The location of the MAKER datastore index log file.

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By default, the output of the gff3_merge is test_genome.all, but you can have an alternate base name for the output files using "-o" option

The final output from gff3_merge is "test_genome.all.gff"

If you don't want to run abinitio gene predictions, then you can run gff3_merge with -n option which do not print fasta sequence in footer. Otherwise, run gff3_merge as above and proceed to abinitio gene prediction step in here

Step 4: Running MAKER-P on the volumes (Recommended)

Since the m1 medium instance size (60GB disk space) selected for running MASTER instance of WQ-MAKER may not be sufficient for most of the MAKER runs, it is recommended to run it on volumes

Step 1: Create a volume

Click the "New" button in the project and select "Create Volume". Enter the name of the volume (MAKER-P-Vol), volume size (200 GB) needed and the provider (Marana) and finally click "Create Volume"

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Step2: Attach the created volume to the MASTER instance

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Step 3: Mount the volume to a specified drive.

Once you have logged in to your instance using Web Shell or ssh of your MAKER-P instance, you must mount your attached volume to access it

Moving data from CyVerse Datastore using iCommands (Optional)

iCommands is a collection of commands for Linux and Mac OS operating systems that are used in the iRODS system to interact with the CyVerse Data Store. Many commands are very similar to Unix utilities. For example, to list files and directories, in Linux you use ls, but in iCommands you use ils.

While iCommands are great for all transfers and for automating tasks via scripts, they are the best choice for large files (2-100 GB each) and for bulk file transfers (many small files). For a comparison of the different methods of uploading and downloading data items, see Downloading and Uploading Data.

iCommands can be used by CyVerse account users to download files that have been shared by other users and to upload files to the Data Store, as well as add metadata, change permissions, and more. Commonly used iCommands are listed below. Follow the instructions on Setting Up iCommands for how to download and configure iCommands for your operating system.